Pattern-Based Data Injection for Sensitive Service Detection
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Solution Overview
Problem
The complexity of provisioning, administering, and managing resources in large distributed systems, particularly in service-oriented architectures, makes it difficult to identify and monitor services that process or store sensitive data, leading to potential security breaches and inefficiencies.
Innovation Solution
A pattern-based detection system using data injection, where specific data values are injected into a distributed system, monitored for interactions, and compared against predefined patterns to identify services handling sensitive data, allowing for real-time visualization and reporting of data flows.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data injection and pattern matching is implemented to identify services processing sensitive data, then security monitoring capability is improved, but system complexity increases
Solution Approach 1:
The patent introduces a data analyzer as an intermediary component that receives service interactions, performs pattern matching, and generates reports. This mediator handles the complexity of security analysis separately from the core service operations, allowing security monitoring to be added without fundamentally restructuring the distributed system architecture.
Solution Approach 2:
The system creates copies of service interactions and data flows for analysis purposes. By duplicating interaction data and sending copies to the data analyzer for pattern matching, the system enables security monitoring without interfering with the original service operations, thus adding monitoring capability while maintaining system independence.
2Measurement precision
If comprehensive service interaction monitoring is implemented, then detection accuracy for sensitive data processing is improved, but processing time increases
Solution Approach 1:
The system performs preliminary actions by injecting test data values into the distributed system before actual operations. These test values are propagated through services in advance, allowing the data analyzer to pre-establish baseline patterns of normal data flow. This preliminary setup enables faster real-time detection during actual operations by comparing against pre-computed patterns.
Solution Approach 2:
The system implements periodic action through scheduled data injection and pattern matching operations. Test data values are injected at regular intervals, and the data analyzer periodically processes service interactions. This periodic approach balances comprehensive monitoring with processing time by not continuously analyzing every single interaction, thus maintaining detection accuracy while controlling processing overhead.
3Productivity
If real-time data flow visualization is implemented, then operational efficiency is improved, but resource consumption increases
Solution Approach 1:
The system applies local quality by providing targeted visualization of data flows only for services and interactions relevant to sensitive data processing. Rather than visualizing all system activity, the data analyzer focuses pattern matching and reporting on specific services that handle sensitive data types. This localized approach improves operational efficiency by highlighting critical areas while reducing overall resource consumption compared to comprehensive system-wide visualization.
Data Source
AI summary
Pattern based detection of data usage is facilitated using data injection. Data values are injected in one or more storage locations accessible to a plurality of services or included in service requests. Service interactions among the services are compared to a set of patterns. The set of patterns are configured to match the data values. By comparing the service interactions to the patterns, one or more of the service interactions are determined to include individual ones of the data values. Data are generated indicating a presence of the data values in the services.